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Two-dimensional (2D) transition metal carbides, nitrides and carbonitrides, known as MXenes, are of interest as electrocatalysts. Tungsten-based MXenes are predicted to have low overpotentials in the hydrogen evolution reaction but their synthesis has proven difficult due to the calculated instability of their hypothetical MAX precursors. In this study, we present a theory-guided synthesis of a tungsten-based MXene, W2TiC2Tx, derived from a non-MAX nanolaminated ternary carbide (W,Ti)4C4−y precursor by the selective etching of one of the covalently bonded tungsten layers. Our results indicate the importance of tungsten and titanium ordering, the presence of vacancy defects in the metal layers, and the lack of oxygen impurities in the carbon layers for the successful selective etching of the precursor. We confirm the atomistic out-of-plane ordering of tungsten and titanium using computational and experimental characterizations. The tungsten-rich basal plane endows W2TiC2Tx MXene with a high electrocatalytic hydrogen evolution reaction performance (∼144 mV overpotential at 10 mA cm−2). This study reports a tungsten-based MXene synthesized from a covalently bonded non-MAX precursor, adding to the synthetic strategies for 2D materials. 

Two-dimensional transition metal carbides, nitrides, and carbonitrides, known as MXenes, hold potential in electrocatalytic applications. Tungsten (W) based-MXenes are of particular interest as they are predicted to have low overpotentials in hydrogen evolution reaction (HER). However, incorporating W into the MXene structure has proven difficult due to the calculated instability of its hypothetical MAX precursors. In this study, we present a theory-guided synthesis of a W-containing MXene, W2TiC2Tx, derived from a non-MAX nanolaminated ternary carbide (W,Ti)4C4-y precursor by selective etching of one of the covalently bonded tungsten layers. Our results indicate the importance of W and Ti ordering and the presence of vacancy defects for the successful selective etching of the precursor. We confirm the atomistic out-of-plane ordering of W and Ti using density functional theory, Rietveld refinement, and electron microscopy methods. Additionally, the W-rich basal plane endows W2TiC2Tx MXene with a remarkable HER overpotential (~144 mV at 10 mA/cm2). This study adds a tungsten-containing MXene made from a covalently bonded non-MAX phase opening more ways to synthesize novel 2D materials. 

Abstract Task‐incremental learning (Task‐IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under‐explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in‐depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time. 

Nitrogen doping in carbon materials can modify the employed carbon material’s electronic and structural properties, which helps in creating a stronger metal-support interaction. In this study, the role of nitrogen doping in improving the durability of Pt catalysts supported on a three-dimensional vertically aligned carbon nanofiber (VACNF) array towards oxygen reduction reaction (ORR) was explored. The nitrogen moieties present in the N-VACNF enhanced the metal-support interaction and contributed to a reduction in the Pt particle size from 3.1 nm to 2.3 nm. The Pt/N-VACNF catalyst showed better durability when compared to Pt/VACNF and Pt/C catalysts with similar Pt loading. DFT calculations validated the increase in the durability of the Pt NPs with an increase in pyridinic N and corroborated the molecular ORR pathway for Pt/N-VACNF. Moreover, the Pt/N-VACNF catalyst was found to have excellent tolerance towards methanol crossover.